1. Technical Field
The subject invention generally relates to decision making. More particularly, the subject invention relates to decision making for team and individual training.
2. Background
Modern training simulation systems present a unique opportunity. Training designers can generate large libraries of experiential training treatments by systematically varying specific parameters that influence the challenge to trainees with respect to training objectives. When those training treatments are scenarios, instructors can choose from this vast library the scenario that is most appropriate to trainees at a given time. More dynamic versions of this vision include parameterized training, in which instructors specify scenario parameters prior to each training event, and adaptive training, which automatically adjusts parameters during training.
This is an opportunity in that it enables instructors to fit the training more tightly to the needs of trainees. It is a significant challenge, however, because it may be quite difficult for a human instructor to reliably predict which of many candidate scenarios will most rapidly advance trainees towards expertise. Given that a team has successfully executed some training scenario that presents a large number of targets and few threats (or some other configuration of these or other parameters), is it appropriate to select a scenario that increases targets while holding threats constant, increases threats while holding targets constant, increases both, or decreases both?
Instructors traditionally address this problem by exploiting instructional principles, such as the use of hierarchical part task training, in which each skill is taught until students achieve some standard of performance, and then the next is taught. Alternatively, computer-based training adapts training to the performance of students based on a fixed set of rules concerning which training conditions to apply given a student state.
Traditional solutions such as hierarchical part task training potentially take more training time to achieve a given level of student performance and/or achieve lower levels of performance given a maximum training time. Opportunities to accelerate and/or improve training effects are not exploited by these solutions.
Traditional solutions such as computer-based training fail when either the student state cannot be accurately judged (i.e., is probabilistic) or the effects of training conditions are uncertain, or both. This is frequently the case in complex domains, team training, and where the number of potential training conditions is large (as in simulation-based training).